


'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import (
     TFDebertaVaForMaskedLM,
     TFDebertaVaForQuestionAnswering,
     TFDebertaVaForSequenceClassification,
     TFDebertaVaForTokenClassification,
     TFDebertaVaModel,
 )

class     _lowerCamelCase       :




 '''simple docstring'''


 def __init__(   self   ,   __lowercase   ,   __lowercase=13   ,   __lowercase=7   ,   __lowercase=True   ,   __lowercase=True   ,   __lowercase=True   ,   __lowercase=True   ,   __lowercase=99   ,   __lowercase=32   ,   __lowercase=2   ,   __lowercase=4   ,   __lowercase=37   ,   __lowercase="gelu"   ,   __lowercase=0.1   ,   __lowercase=0.1   ,   __lowercase=512   ,   __lowercase=16   ,   __lowercase=2   ,   __lowercase=0.0_2   ,   __lowercase=False   ,   __lowercase=True   ,   __lowercase="None"   ,   __lowercase=3   ,   __lowercase=4   ,   __lowercase=None   ,   ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      parent
  __A	:		Union[str, Any]	  =      batch_size
  __A	:		Optional[int]	  =      seq_length
  __A	:		Tuple	  =      is_training
  __A	:		Dict	  =      use_input_mask
  __A	:		Optional[Any]	  =      use_token_type_ids
  __A	:		int	  =      use_labels
  __A	:		Tuple	  =      vocab_size
  __A	:		str	  =      hidden_size
  __A	:		Tuple	  =      num_hidden_layers
  __A	:		str	  =      num_attention_heads
  __A	:		Optional[int]	  =      intermediate_size
  __A	:		Any	  =      hidden_act
  __A	:		List[Any]	  =      hidden_dropout_prob
  __A	:		List[Any]	  =      attention_probs_dropout_prob
  __A	:		Any	  =      max_position_embeddings
  __A	:		Optional[int]	  =      type_vocab_size
  __A	:		str	  =      type_sequence_label_size
  __A	:		List[str]	  =      initializer_range
  __A	:		Optional[int]	  =      num_labels
  __A	:		Dict	  =      num_choices
  __A	:		Optional[int]	  =      relative_attention
  __A	:		Optional[Any]	  =      position_biased_input
  __A	:		Tuple	  =      pos_att_type
  __A	:		int	  =      scope


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size    )

  __A	:		List[Any]	  =      None
  if self.use_input_mask:
   __A	:		int	  =      random_attention_mask([self.batch_size, self.seq_length]    )

  __A	:		Optional[int]	  =      None
  if self.use_token_type_ids:
   __A	:		Any	  =      ids_tensor([self.batch_size, self.seq_length]   ,   self.type_vocab_size    )

  __A	:		Tuple	  =      None
  __A	:		int	  =      None
  __A	:		int	  =      None
  if self.use_labels:
   __A	:		Dict	  =      ids_tensor([self.batch_size]   ,   self.type_sequence_label_size    )
   __A	:		Union[str, Any]	  =      ids_tensor([self.batch_size, self.seq_length]   ,   self.num_labels    )

  __A	:		int	  =      DebertaVaConfig(
      vocab_size=self.vocab_size   ,   hidden_size=self.hidden_size   ,   num_hidden_layers=self.num_hidden_layers   ,   num_attention_heads=self.num_attention_heads   ,   intermediate_size=self.intermediate_size   ,   hidden_act=self.hidden_act   ,   hidden_dropout_prob=self.hidden_dropout_prob   ,   attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,   max_position_embeddings=self.max_position_embeddings   ,   type_vocab_size=self.type_vocab_size   ,   relative_attention=self.relative_attention   ,   position_biased_input=self.position_biased_input   ,   initializer_range=self.initializer_range   ,   return_dict=__lowercase   ,   )

  return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		List[Any]	  =      TFDebertaVaModel(config=__lowercase    )
  __A	:		Optional[Any]	  =      {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

  __A	:		int	  =      [input_ids, input_mask]
  __A	:		Optional[Any]	  =      model(__lowercase    )

  __A	:		List[Any]	  =      model(__lowercase    )

  self.parent.assertEqual(result.last_hidden_state.shape   ,   (self.batch_size, self.seq_length, self.hidden_size)    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		int	  =      TFDebertaVaForMaskedLM(config=__lowercase    )
  __A	:		Optional[int]	  =      {
      'input_ids': input_ids,
      'attention_mask': input_mask,
      'token_type_ids': token_type_ids,
  }
  __A	:		Optional[Any]	  =      model(__lowercase    )
  self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.vocab_size)    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Dict	  =      self.num_labels
  __A	:		List[Any]	  =      TFDebertaVaForSequenceClassification(config=__lowercase    )
  __A	:		Dict	  =      {
      'input_ids': input_ids,
      'attention_mask': input_mask,
      'token_type_ids': token_type_ids,
  }

  __A	:		Union[str, Any]	  =      model(__lowercase    )
  self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.num_labels)    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Optional[int]	  =      self.num_labels
  __A	:		Optional[int]	  =      TFDebertaVaForTokenClassification(config=__lowercase    )
  __A	:		List[str]	  =      {
      'input_ids': input_ids,
      'attention_mask': input_mask,
      'token_type_ids': token_type_ids,
  }
  __A	:		Optional[int]	  =      model(__lowercase    )
  self.parent.assertEqual(result.logits.shape   ,   (self.batch_size, self.seq_length, self.num_labels)    )


 def 			snake_case__ (   self   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase   ,   __lowercase    ):

  """simple docstring"""







  __A	:		Any	  =      TFDebertaVaForQuestionAnswering(config=__lowercase    )
  __A	:		Tuple	  =      {
      'input_ids': input_ids,
      'attention_mask': input_mask,
      'token_type_ids': token_type_ids,
  }

  __A	:		Dict	  =      model(__lowercase    )
  self.parent.assertEqual(result.start_logits.shape   ,   (self.batch_size, self.seq_length)    )
  self.parent.assertEqual(result.end_logits.shape   ,   (self.batch_size, self.seq_length)    )







 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.prepare_config_and_inputs()
  (
      (
      __A   
  )  ,(
      __A   
  )  ,(
      __A   
  )  ,(
      __A   
  )  ,(
      __A   
  )  ,(
      __A   
  )  ,(
      __A   
  )  ,   
  )	:		Dict	  =      config_and_inputs
  __A	:		List[Any]	  =      {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
  return config, inputs_dict


@require_tf
class     _lowerCamelCase       (     snake_case_     ,		snake_case_     ,		unittest.TestCase					):




 '''simple docstring'''
 __lowercase					:		Optional[Any]     				=    (
     (
         TFDebertaVaModel,
         TFDebertaVaForMaskedLM,
         TFDebertaVaForQuestionAnswering,
         TFDebertaVaForSequenceClassification,
         TFDebertaVaForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 __lowercase					:		List[Any]     				=    (
     {
         """feature-extraction""": TFDebertaVaModel,
         """fill-mask""": TFDebertaVaForMaskedLM,
         """question-answering""": TFDebertaVaForQuestionAnswering,
         """text-classification""": TFDebertaVaForSequenceClassification,
         """token-classification""": TFDebertaVaForTokenClassification,
         """zero-shot""": TFDebertaVaForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )

 __lowercase					:		Optional[Any]     				=    False
 __lowercase					:		List[Any]     				=    False


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      TFDebertaVaModelTester(self    )
  __A	:		int	  =      ConfigTester(self   ,   config_class=__lowercase   ,   hidden_size=37    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  self.config_tester.run_common_tests()


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Dict	  =      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_masked_lm(*__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Union[str, Any]	  =      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_question_answering(*__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		List[str]	  =      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_sequence_classification(*__lowercase    )


 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Dict	  =      self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_token_classification(*__lowercase    )







 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Tuple	  =      TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'    )
  self.assertIsNotNone(__lowercase    )


@require_tf
class     _lowerCamelCase       (     unittest.TestCase					):




 '''simple docstring'''


 @unittest.skip(reason='Model not available yet'    )
 def 			snake_case__ (   self    ):

  """simple docstring"""







  pass







 @slow
 def 			snake_case__ (   self    ):

  """simple docstring"""







  __A	:		Any	  =      TFDebertaVaModel.from_pretrained('kamalkraj/deberta-v2-xlarge'    )
  __A	:		Optional[int]	  =      tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]    )
  __A	:		List[str]	  =      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]    )
  __A	:		Union[str, Any]	  =      model(__lowercase   ,   attention_mask=__lowercase    )[0]

  __A	:		Optional[Any]	  =      tf.constant(
      [[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]]    )
  tf.debugging.assert_near(output[:, 1:4, 1:4]   ,   __lowercase   ,   atol=1E-4    )







"""simple docstring"""



import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


SCREAMING_SNAKE_CASE__:Optional[Any]									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase						):
        super().__init__()
        __a       =    nn.ModuleList(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = True    ,		):
        for i, (image, scale, controlnet) in enumerate(zip(lowerCamelCase    ,		lowerCamelCase    ,		self.nets						)						):
               __a ,   __a       =    controlnet(
                   lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		)

               # merge samples
               if i == 0:
                      __a ,   __a       =    down_samples, mid_sample
               else:
                      __a       =    [
                          samples_prev + samples_curr
                          for samples_prev, samples_curr in zip(lowerCamelCase    ,		lowerCamelCase						)
                      ]
                      mid_block_res_sample += mid_sample

        return down_block_res_samples, mid_block_res_sample


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = True    ,		lowerCamelCase = None    ,		lowerCamelCase = False    ,		lowerCamelCase = None    ,		):
        __a       =    0
        __a       =    save_directory
        for controlnet in self.nets:
               controlnet.save_pretrained(
                   lowerCamelCase    ,		is_main_process=lowerCamelCase    ,		save_function=lowerCamelCase    ,		safe_serialization=lowerCamelCase    ,		variant=lowerCamelCase    ,		)

               idx += 1
               __a       =    model_path_to_save + F"_{idx}"






 @classmethod
 def 						a__   ( cls    ,		lowerCamelCase    ,		**lowerCamelCase						):
        __a       =    0
        __a       =    []

        # load controlnet and append to list until no controlnet directory exists anymore
        # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
        # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
        __a       =    pretrained_model_path
        while os.path.isdir(lowerCamelCase						):
               __a       =    ControlNetModel.from_pretrained(lowerCamelCase    ,		**lowerCamelCase						)
               controlnets.append(lowerCamelCase						)

               idx += 1
               __a       =    pretrained_model_path + F"_{idx}"

        logger.info(F"{len(lowerCamelCase						)} controlnets loaded from {pretrained_model_path}."						)

        if len(lowerCamelCase						) == 0:
               raise ValueError(
                   F"No ControlNets found under {os.path.dirname(lowerCamelCase						)}. Expected at least {pretrained_model_path + '_0'}."						)

        return cls(lowerCamelCase						)
